Alreem Alothman

Highly motivated Computer Science student with practical experience in Cybersecurity, Data Analysis, and Game Development. Proven track record of delivering technical projects, including automated Arabic fact-checking systems and 3D game environments. Skilled in Python, Java, and Unity, with a strong background in leadership as a Vice-Captain for university debate events

• Debating & Public Speaking (Vice-Captain of Debate Club), Community Volunteering (Data analysis for non-profits),AI Research & Emerging Tech, Strategic Gaming & Game Design.

• Top 30% Rank – SDAIA Datathon (2023): Recognized for high performance in the Arabic Document Classification challenge, optimizing machine learning models to achieve an 85% F1-score.+1
• Founding Member Recognition – Enjaz Club: Honored as one of the first 3 members to launch the club, leading AI research initiatives and weekly knowledge sessions.+1
• Community Impact Achievement – Kaswat Farah Association: Successfully optimized clothing distribution for 100+ families, reducing surplus waste by 15% through data analysis.

• Game Development with Unity Bootcamp – Tuwaiq Academy (Sep 2025 – Nov 2025): Completed 300 intensive training hours developing 5 comprehensive game projects using C# and Unity Engine.+1
• Advanced Python Programming – Coursera (2025):.
• Swift UI/UX Design – Satr (2025):.

• Tuwaiq Game Development Portfolio (3 Months):
  Developed 5 comprehensive game projects using the Unity Engine and C# scripting.
  Designed 3D environments, implemented core Game Mechanics, and programmed Enemy AI.
  Optimized game performance and integrated interactive UI/UX features.
• Automated Arabic Fact-Checking System:
  Built a classification model to identify claims as true, false, or misleading with 89% accuracy.
  Fine-tuned BERT for contextual understanding of Arabic text.
• Graduation Project: KnightHoot (AI Scam Detection) Developed an AI-powered real-time system to detect scams across phone calls, SMS, emails, and URLs in both Arabic and English. Engineered and deployed lightweight Machine Learning models (Extra Trees & Random Forest) as scalable cloud APIs using FastAPI, achieving 98% accuracy. Built the cross-platform mobile application using Expo and Node.js, integrating a Supabase database for real-time data management. Designed the system with a hybrid microservices architecture to ensure high performance and user privacy.